

import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase_							:					Optional[Any]		 =   logging.get_logger(__name__)


UpperCAmelCase_							:					str		 =   '''▁'''

UpperCAmelCase_							:					List[Any]		 =   {'''vocab_file''': '''spiece.model'''}

UpperCAmelCase_							:					Any		 =   {
    '''vocab_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'''
        )
    }
}

UpperCAmelCase_							:					int		 =   {
    '''google/reformer-crime-and-punishment''': 524_288,
}






class      lowerCamelCase_      (	_lowercase	):
	_lowercase		:   Union[str, Any]     					=				VOCAB_FILES_NAMES
	_lowercase		:   Dict     					=				PRETRAINED_VOCAB_FILES_MAP
	_lowercase		:   List[Any]     					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_lowercase		:   Optional[int]     					=				['''input_ids''', '''attention_mask''']




	def __init__(     self						:     List[Any]					,      __A						:     Tuple					,      __A						:     Dict="</s>"					,      __A						:     Any="<unk>"					,      __A						:     Any=[]					,      __A						:     Optional[Dict[str, Any]] = None					,      **__A						:     Tuple					,      ):
		__A :			Optional[Any]		    =			{} if sp_model_kwargs is None else sp_model_kwargs

		super().__init__(
		    eos_token=__A					,      unk_token=__A					,      additional_special_tokens=__A					,      sp_model_kwargs=self.sp_model_kwargs					,      **__A					,      )

		__A :			str		    =			vocab_file
		__A :			Tuple		    =			spm.SentencePieceProcessor(**self.sp_model_kwargs							)
		self.sp_model.Load(__A							)




	@property
	def        lowerCAmelCase_       (     self						:     Dict							):
		return self.sp_model.get_piece_size()




	def        lowerCAmelCase_       (     self						:     Optional[Any]							):
		__A :			Optional[int]		    =			{self.convert_ids_to_tokens(__A							): i for i in range(self.vocab_size							)}
		vocab.update(self.added_tokens_encoder							)
		return vocab




	def __getstate__(     self						:     Optional[int]							):
		__A :			Dict		    =			self.__dict__.copy()
		__A :			Tuple		    =			None
		return state




	def __setstate__(     self						:     int					,      __A						:     List[Any]							):
		__A :			str		    =			d

		# for backward compatibility
		if not hasattr(self					,      """sp_model_kwargs"""							):
			__A :			Union[str, Any]		    =			{}

		__A :			Union[str, Any]		    =			spm.SentencePieceProcessor(**self.sp_model_kwargs							)
		self.sp_model.Load(self.vocab_file							)




	def        lowerCAmelCase_       (     self						:     str					,      __A						:     str							):
		return self.sp_model.encode(__A					,      out_type=__A							)




	def        lowerCAmelCase_       (     self						:     Union[str, Any]					,      __A						:     str							):
		return self.sp_model.piece_to_id(__A							)




	def        lowerCAmelCase_       (     self						:     Any					,      __A						:     List[str]							):
		if index < self.sp_model.get_piece_size():
			__A :			Any		    =			self.sp_model.IdToPiece(__A							)
		return token




	def        lowerCAmelCase_       (     self						:     Optional[Any]					,      __A						:     Tuple							):
		__A :			Optional[Any]		    =			[]
		__A :			Optional[int]		    =			""""""
		for token in tokens:
			# make sure that special tokens are not decoded using sentencepiece model
			if token in self.all_special_tokens:
				out_string += self.sp_model.decode(__A							) + token
				__A :			Union[str, Any]		    =			[]
			else:
				current_sub_tokens.append(__A							)
		out_string += self.sp_model.decode(__A							)
		return out_string.strip()




	def        lowerCAmelCase_       (     self						:     Tuple					,      __A						:     str					,      __A						:     Optional[str] = None							):
		if not os.path.isdir(__A							):
			logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""							)
			return
		__A :			str		    =			os.path.join(
		    __A					,      (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)

		if os.path.abspath(self.vocab_file							) != os.path.abspath(__A							) and os.path.isfile(self.vocab_file							):
			copyfile(self.vocab_file					,      __A							)
		elif not os.path.isfile(self.vocab_file							):
			with open(__A					,      """wb"""							) as fi:
				__A :			Any		    =			self.sp_model.serialized_model_proto()
				fi.write(__A							)

		return (out_vocab_file,)









from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE     :					Optional[Any]      =       logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE     :					int      =       {
    "facebook/deit-base-distilled-patch16-224": (
        "https://huggingface.co/facebook/deit-base-patch16-224/resolve/main/config.json"
    ),
    # See all DeiT models at https://huggingface.co/models?filter=deit
}

class     A__   (       snake_case__      ):







    """simple docstring"""

    __magic_name__         =						'deit'
    def __init__(			self     ,   __snake_case=7_6_8     ,   __snake_case=1_2     ,   __snake_case=1_2     ,   __snake_case=3_0_7_2     ,   __snake_case="gelu"     ,   __snake_case=0.0     ,   __snake_case=0.0     ,   __snake_case=0.02     ,   __snake_case=1E-12     ,   __snake_case=2_2_4     ,   __snake_case=1_6     ,   __snake_case=3     ,   __snake_case=True     ,   __snake_case=1_6     ,   **__snake_case     ,   ):
         super().__init__(**__snake_case       )

         snake_case											=							hidden_size
         snake_case											=							num_hidden_layers
         snake_case											=							num_attention_heads
         snake_case											=							intermediate_size
         snake_case											=							hidden_act
         snake_case											=							hidden_dropout_prob
         snake_case											=							attention_probs_dropout_prob
         snake_case											=							initializer_range
         snake_case											=							layer_norm_eps
         snake_case											=							image_size
         snake_case											=							patch_size
         snake_case											=							num_channels
         snake_case											=							qkv_bias
         snake_case											=							encoder_stride






class     A__   (       snake_case__      ):







    """simple docstring"""

    __magic_name__         =						version.parse('1.11'      )
    @property
    def 						a_     (			self       ):
         return OrderedDict(
             [
                 ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
             ]       )




    @property
    def 						a_     (			self       ):
         return 1E-4




